Unsupervised Clustering of Immunohistochemical Markers to Define High-Risk Endometrial Cancer

In-depth comparative analysis of Illumina® MiSeq run metrics: Development of a wet-lab quality assessment tool

Whole genome sequencing of bacterial isolates has become a daily task in many laboratories, generating incredible amounts of data. However, data acquisition is not an end in itself; the goal is to acquire high-quality data useful for understanding genetic relationships. Having a method that could rapidly determine which of the many available run metrics are the most important indicators of overall run quality and having a way to monitor these during a given sequencing run would be extremely helpful to this effect. Therefore, we compared various run metrics across 486 MiSeq runs, from five different machines. By performing a statistical analysis using principal components analysis and a K-means clustering algorithm of the metrics, we were able to validate metric comparisons among instruments, allowing for the development of a predictive algorithm, which permits one to observe whether a given MiSeq run has performed adequately. This algorithm is available in an Excel spreadsheet: that is, MiSeq Instrument & Run (In-Run) Forecast. Our tool can help verify that the quantity/quality of the generated sequencing data consistently meets or exceeds recommended manufacturer expectations. Patterns of deviation from those expectations can be used to assess potential run problems and plan preventative maintenance, which can save valuable time and funding resources.

Collagen IV and laminin-1 expression in embryonic mouse lens using principal components analysis technique

Immunohistochemistry section staining is not always easy to interpret. Manual quantification of immunohistochemical staining is limited by the observer visual ability to detect changes in level staining. Hence, the quantification of immunostaining by means of digital image analysis allows us to measure accurately protein expression percentages in immunobiological stained tissues and ensures to overcome the visual limitations. We perform an experimental study to analyse the impact of folic acid (FA) deficiency into collagen IV and laminin-1 expression in the embryonic mouse lens. The study starts with microscope images of embryos mouse lens whose mothers fed a diet deficient in FA during 2 and 8 weeks. A principal component analysis (PCA) image processing is used to analyse these images coming from control and FA deficit groups. The method permits to define an index of over- or infraexpression of collagen IV and laminin-1 associated to different spatial organisation structures (PC processes). Additionally, it permits to determine in precise percentage the exact quantity of the overexpression or infraexpression and finally to comprehend molecular regionalisation and expression in both control and deficient groups. The results suggest that even with 2 weeks of deficit of FA the expression and distribution of both molecules is affected.